ABSTRACT For systems with an accelerator component, it is important from an operational and planning perspective to understand how and to what extent the accelerators are being used. Having a framework for tracking the utilization of accelerator resources is important both for judging how efficiently used a system is and for capacity and configuration planning of future systems. In addition to tracking total utilization and accelerator efficiency numbers, some attention should also be paid to the types of research and workflows that are being executed on the system. In the past, the demand for accelerator resources was largely driven by more traditional simulation codes, such as molecular dynamics. But with the growing popularity of deep learning and artificial intelligence workflows, accelerators have become even more highly sought after and are being used in new ways. Provisioning resources to researchers via an allocation system allows sites to track a project's usage and workflow as well as the scientific impact of the project. With such tools and data in hand, characterizing the GPU utilization of deep learning frameworks versus more traditional GPU‐enabled applications becomes possible. In this paper we present a survey of GPU monitoring tools used in sites and a framework for tracking the utilization of NVIDIA GPUs on Slurm‐scheduled HPC systems used at Indiana University. We also present an analysis of accelerator utilization on multiple systems, including an HPE Apollo system targeting AI workflows and a Cray EX system.

In this special section we bring you a practice and experience effort in reproducibility for large-scale computational science at SC20. This section includes nine critiques, each by a student team that reproduced results from a paper published at SC19, during the following year’s Student Cluster Competition. The paper is also included in this section and has been expanded upon, now including an analysis of the outcomes of the students’ reproducibility experiments. Lastly, this special section encapsulates a variety of advances in reproducibility in the SC conference series technical program.

Summary The 10 best papers from 77 submissions to the Cray User Group (CUG) 2017 conference, held in Redmond, Washington, are presented in this issue. Several themes of the conference are highlighted in this group of articles, including the Knight's Landing Architecture, Optimization of I/O, and data analytics.

This deposit includes (1) the invitation and reminder messages sent to the survey sample and (2) the survey questions, including instructions for branching.

SUMMARY The computational demands of an ever increasing number of scholars at universities and research institutions throughout the country are outgrowing the capacity of desktop workstations. Researchers are turning to high performance computing facilities, both on their campuses and at regional and national centers, to run simulations and analyze data. The Extreme Science and Engineering Discovery Environment (XSEDE) is one of the first places researchers turn to when they outgrow their campus resources. XSEDE machines are far larger (by at least an order of magnitude) than what most universities offer. Transitioning from a campus resource to an XSEDE resource is seldom a trivial task. XSEDE has taken many steps to make this transition easier, including the campus bridging initiative, the Campus Champions program, and the Extended Collaborative Support Service program. In this paper, we present a new facet to the campus bridging initiative in the form of the campus bridging expert, an information technology professional dedicated to aid researchers in transitioning from desktop, to campus, to regional, and to national resources. We outline the current state of affairs and explore how campus bridging experts could provide maximal impact for minimal investment on the part of the organizing body. Copyright © 2014 John Wiley & Sons, Ltd.

As part of the SCinet Research Sandbox at the IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC11), Indiana University utilized a dedicated 100 Gbps wide area network (WAN) link spanning more than 3,500 km (2,175 mi) to demonstrate the capabilities of the Lustre high performance parallel file system in a high bandwidth, high latency WAN environment. This demonstration functioned as a proof of concept and provided an opportunity to study Lustre's performance over a 100 Gbps WAN. To characterize the performance of the network and file system a series of benchmarks and tests were undertaken. These included low level iperf network tests, Lustre networking tests, file system tests with the IOR benchmark, and a suite of real-world applications reading and writing to the file system. All of the tests and benchmarks were run over a the WAN link with a latency of 50.5 ms. In this article we describe the configuration and constraints of the demonstration and focus on the key findings regarding the networking layer for this extremely high bandwidth and high latency connection. Of particular interest are the challenges presented by link aggregation for a relatively small number of high bandwidth connections, and the specifics of virtual local area network routing for 100 Gbps routing elements.

As part of the SCinet Research Sandbox at the Supercomputing 2011 conference, Indiana University (IU) demonstrated use of the Lustre high performance parallel file system over a dedicated 100 Gbps wide area network (WAN) spanning more than 3,500 km (2,175 mi). This demonstration functioned as a proof of concept and provided an opportunity to study Lustre's performance over a 100 Gbps WAN. To characterize the performance of the network and file system, low level iperf network tests, file system tests with the IOR benchmark, and a suite of real-world applications reading and writing to the file system were run over a latency of 50.5 ms. In this article we describe the configuration and constraints of the demonstration and outline key findings.

In this article we propose a revolutionary new paradigm for the processing and analysis of astronomical image data. We describe a blueprint for a centralized data repository and processing system, which leverages national cyber infrastructure. Included is a brief discussion of the current paradigm in astronomical image processing. The upcoming One Degree Imager (ODI) instrument, to be installed at the Wisconsin, Indiana, Yale and NOAO (WIYN) observatory in 2011, is examined as an ideal use case. Details on the major components and a detailed workflow in the case of data processing for the ODI instrument are highlighted.

Astrophysical simulations of protoplanetary disks and gas giant planet formation are being performed with a variety of numerical methods. Some of the codes in use today have been producing scientifically significant results for several years, or even decades. Each must simulate millions of resolution elements for millions of time steps, capture and store output data, and rapidly and efficiently analyze this data. To do this effectively, a parallel code is needed that scales to tens or hundreds of processors. Furthermore, an efficient workflow for the transport, analysis, and interpretation of the output data is needed to achieve scientifically meaningful results.